Software Engineer II, Data

As a Software Engineer II, Data at FiscalNote, you will design and develop solutions for complex problems, write maintainable code, improve our architecture, and build data services for efficient processing of both batch and real-time data. You'll also work on internal tools to enhance data review and support the overall health and quality of our data pipelines.

Candidates interested in the Software Engineer II, Data position at FiscalNote should have 3-5 years of professional experience in software development, strong SQL skills, and a solid understanding of relational databases. Proficiency in Python is preferred, alongside experience with networked architecture, version control systems like git, and automated testing frameworks.

To excel as a Software Engineer II, Data at FiscalNote, you should be proficient in SQL and have a strong understanding of relational databases, especially PostgreSQL or MySQL. Experience with cloud platforms like AWS, GCP, or Azure is advantageous, as is familiarity with containerization tools such as Docker and Kubernetes.

Collaboration is essential in the Data Engineering team at FiscalNote. As a Software Engineer II, Data, you'll work closely with product, UI/UX teams, and other engineering team members, ensuring that all developments are in line with user needs and project goals. Clear communication is vital for effective teamwork on ambitious projects.

In the Software Engineer II, Data role at FiscalNote, you can anticipate working on diverse and ambitious projects, including building data processing services, enhancing monitoring tools for data quality, and developing internal tools for data review and augmentation. These projects will push your technical limits and encourage creative solutions.

When discussing your experience with data processing systems, highlight specific projects where you designed, built, or maintained these systems. Emphasize your role in integrating both batch and real-time data processing and mention any tools or technologies you've used, such as APIs or message-based communication.

To ensure code quality, I implement rigorous testing practices, including automated tests and code reviews. I also focus on writing readable and maintainable code by adhering to best practices and leveraging version control systems like Git effectively.

In collaborating with cross-functional teams, I prioritize clear communication and active listening. I strive to understand the perspectives of product managers and UI/UX designers to align our technical solutions with user needs, ensuring that we achieve project goals together.

When faced with a challenging technical problem, I first break down the issue into smaller components to understand it better. Then, I explore different solutions by considering trade-offs, consulting with peers, and reviewing existing documentation. After selecting the most viable option, I implement it methodically, monitoring results to ensure effectiveness.

My experience with relational databases includes designing optimized schemas, writing complex queries, and ensuring data integrity. I have worked extensively with PostgreSQL and MySQL, focusing on performance improvements for data retrieval and processing tasks.

I stay current with new technologies in data engineering by regularly attending webinars, participating in online communities, and reading industry blogs and publications. This helps me stay informed about the latest tools and methods for improving data processing and analysis.

I have hands-on experience with cloud computing platforms like AWS, where I've utilized services such as EC2 for server deployment, S3 for data storage, and Lambda for serverless computing. I appreciate the flexibility and scalability these platforms offer for developing robust data services.

To manage project ambiguity, I adopt a proactive approach by seeking clarification whenever possible and gathering as much context as I can from stakeholders. I focus on defining the project's core objectives and iteratively refining our goals based on feedback and new insights.

I optimize performance in data pipelines by implementing efficient data structures and algorithms, leveraging parallel processing where possible, and monitoring system performance to identify bottlenecks. Regularly reviewing and refactoring code is also crucial for maintaining optimal performance.

When faced with the need to learn a new technology quickly, I immerse myself in comprehensive learning materials, including official documentation, tutorials, and community forums. I also engage in hands-on projects to apply my new knowledge, which accelerates the learning process and boosts my confidence in using the technology effectively.